If you have never used LaTex, you may want to consult with more
senior students how to set yourself up and get a gentle introduction,
or you can install free software and documentation from
the following sources:

In the manual, pay special attention to Section 1.3.2 (special
characters) and Chapter 3 for math typesetting (math symbols:
Section 3.10). To produce PDF from LaTex, the LEd environment
requires you to click the green and blue right arrows in the tool
bar. Feel free to check out other free software and other
documents. If you find something particularly useful, please, let
the instructor know.

IMPORTANT: If a function in the class notes does not work or is not
found in your R session, check whether the function is in one
of the R code files below. If so, download and read the file into R
one more time, even if you thought you had done so earlier.
I allow myself to update the code all the time.

Background Papers:

The paper on tree-based regression and classification
is in this PDF file.

Undergraduate students contemplating this course: If you
do not have a solid background in statistics and linear algebra
already as well as R programming experience, you should not take
this class or, at a minimum, not rely on credit from it for
graduation. As mentioned above, the goal is to prepare students for
statistics research, and there will be only one standard of
performance for all students.

Publication quality writing and mathematical typesetting are of
utmost importance for statistics research. To get used to the
standards of writing research papers in statistics, some homeworks
will be required to be typeset in LaTex and submitted by e-mail
as a PDF file. You will have to learn LaTex on your own with the help
of other graduate students, but getting started with Latex
will be facilitated by templates provided by the instructor, so all
you need to do is cannibalize the templates by filling in your
solutions.

If you need more reading about R, look up the numerous
books about R
or the numerous free
web documents about R.
Yet another way to find R introductions is to do a search for
"Introduction to R". If you find something particularly
useful, please, let the instructor know.

Other recommended texts:

For regression: Seber and Lee, "Linear Regression Analysis"
(Wiley Series in Probability and Statistics)

For linear algebra: Strang, "Linear Algebra and its Applications" (Academic Press)
Strangely, the most fundamental material is no longer in the recent edition:
"Linear Transformations, Matrices, and Change of Basis."
In older editions this used to be tucked away in the appendix.
For this reason, the material is now included in Homework 2:
You get to derive it yourself by following instructions.